Railway geometry parameter real-time measuring method and device

Abstract

The application discloses a track geometric parameter real-time measurement method and device, and the method comprises the following steps: collecting inertial measurement unit (IMU) data and vertical displacement data at equal spatial intervals; determining the speed of a global navigation satellite system (GNSS) antenna corresponding to the moment of receiving the Nth second pulse signal by using Doppler velocity measurement; correcting the third speed and the third attitude information of the measured carrier according to the speed of the GNSS antenna corresponding to the moment of receiving the Nth second pulse signal by using an extended Kalman filtering algorithm, so as to obtain the fourth speed and the fourth attitude information of the measured carrier corresponding to the moment of the Nth second pulse signal; and determining the track geometric parameter according to the vertical displacement data, the corrected second speed and the second attitude information of the measured carrier. The application corrects the inertial recursive value by using the speed of the GNSS antenna, improves the accuracy of the speed and the attitude information of the measured carrier, and improves the accuracy of the track geometric parameter measurement.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of computer technology, and in particular to a track geometry parameter real-time measurement method and device. BACKGROUND

[0002] The track detection system is generally installed on a comprehensive detection train or a special track inspection vehicle, and uses an inertial measurement unit combined with a laser camera sensor to realize real-time non-contact measurement of internal track geometry parameters such as height, track direction, track gauge, super-elevation, level and triangular pits, and to output detection reports and overrun reports for determining the track line state.

[0003] With the continuous development of high-speed railways, the train speed is continuously improved, and track long-wave irregularity diseases gradually attract the attention of high-speed railway maintenance sections, and need to be dynamically monitored. Since the track irregularity output result takes spatial distance as the horizontal coordinate axis, the track detection system triggers and collects IMU and visual ranging unit data at spatial distance, which is beneficial to guarantee the spatial consistency of measurement values at different speeds. This leads to the change of the sensor sampling rate with the speed. Therefore, guaranteeing the synchronization of sensor data in the equal spatial sampling mode is an urgent problem to be solved in the application of multi-source sensor data fusion. The existing method low-pass filters the IMU data and then performs spatial sampling to avoid high-frequency aliasing caused by vibration noise and electrical noise. For digital IMU sensors, the filtering process reduces the bandwidth of the signal, which affects the accuracy of track geometry parameter measurement in the equal spatial interval sampling.

[0004] In summary, there is an urgent need for a track geometry parameter real-time measurement method to solve the problems existing in the prior art. SUMMARY

[0005] The embodiment of the present application provides a track geometry parameter real-time measurement method to improve the accuracy of track geometry parameter measurement in the equal spatial interval sampling, which comprises the following steps:

[0006] Collecting inertial measurement unit (IMU) data and vertical displacement data at equal spatial intervals; the vertical displacement data is the distance from the ranging sensor to the track top point;

[0007] Determining the first speed and first attitude information of the measured carrier corresponding to each sampling time according to the IMU data;

[0008] Correcting the first speed and first attitude information of the measured carrier corresponding to each sampling time by using an extended Kalman filtering algorithm to obtain the second speed and second attitude information of the measured carrier at the sampling time;

[0009] When the Nth second pulse signal is received, determining the Nth time interval between the time when the Nth second pulse signal is received and the adjacent sampling time;

[0010] determining the third speed and the third attitude information of the measured carrier corresponding to the time of receiving the Nth second pulse signal according to the IMU data of the Nth time interval and the adjacent sampling time;

[0011] determining the speed of the GNSS antenna corresponding to the time of receiving the Nth second pulse signal by Doppler velocity measurement;

[0012] correcting the third speed and the third attitude information of the measured carrier according to the speed of the GNSS antenna corresponding to the time of receiving the Nth second pulse signal by using the extended Kalman filtering algorithm, to obtain the fourth speed and the fourth attitude information of the measured carrier corresponding to the time of receiving the Nth second pulse signal; wherein N is a positive integer greater than 1; and correcting the second speed and the second attitude information of the measured carrier at the sampling time according to the fourth speed and the fourth attitude information of the measured carrier corresponding to the time of receiving the Nth second pulse signal;

[0013] determining the track geometric parameters according to the vertical displacement data, the corrected second speed and the corrected second attitude information of the measured carrier.

[0014] The embodiment of the present application also provides a track geometric parameter real-time measurement device for improving the accuracy of track geometric parameter measurement under equal spatial interval sampling, which comprises:

[0015] a data acquisition module, configured to acquire the IMU data and the vertical displacement data at equal spatial intervals; the vertical displacement data is the distance between the ranging sensor and the track top point;

[0016] The data processing module is configured to determine first speed and first attitude information of the measured carrier corresponding to each sampling time according to the IMU data; correct the first speed and the first attitude information of the measured carrier corresponding to each sampling time by using an extended Kalman filtering algorithm to obtain second speed and second attitude information of the measured carrier at the sampling time; determine an Nth time interval between a time when the Nth second pulse signal is received and an adjacent sampling time when the Nth second pulse signal is received; determine third speed and third attitude information of the measured carrier corresponding to the time when the Nth second pulse signal is received by performing inertial recursion according to the Nth time interval and IMU data of the adjacent sampling time; determine speed of a global navigation satellite system (GNSS) antenna corresponding to the time when the Nth second pulse signal is received by using Doppler velocity measurement; correct the third speed and the third attitude information of the measured carrier by using an extended Kalman filtering algorithm according to the speed of the GNSS antenna corresponding to the time when the Nth second pulse signal is received to obtain fourth speed and fourth attitude information of the measured carrier corresponding to the time when the Nth second pulse signal is received; wherein N is a positive integer greater than 1; correct the second speed and the second attitude information of the measured carrier at the sampling time according to the fourth speed and the fourth attitude information of the measured carrier corresponding to the time when the Nth second pulse signal is received; and determine the track geometric parameters according to the vertical displacement data, the corrected second speed and the second attitude information of the measured carrier.

[0017] The embodiment of the present application also provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor implements the track geometric parameter real-time measurement method when the computer program is executed.

[0018] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program, and the computer program implements the track geometric parameter real-time measurement method when the computer program is executed by a processor.

[0019] The embodiment of the present application also provides a computer program product, which comprises a computer program, and the computer program implements the track geometric parameter real-time measurement method when the computer program is executed by a processor.

[0020] In the embodiment of the present application, the inertial measurement unit (IMU) data and the vertical displacement data are collected at equal spatial intervals; the vertical displacement data is the distance of the ranging sensor from the track top point; the first speed and the first attitude information of the measured carrier at each sampling time are determined according to the IMU data; the first speed and the first attitude information of the measured carrier at each sampling time are corrected to obtain the second speed and the second attitude information of the measured carrier at the sampling time; when the Nth second pulse signal is received, the Nth time interval between the time when the Nth second pulse signal is received and the adjacent sampling time is determined; the third speed and the third attitude information of the measured carrier at the time when the Nth second pulse signal is received are determined by inertial recursion according to the Nth time interval and the IMU data of the adjacent sampling time; the speed of the global navigation satellite system (GNSS) antenna at the time when the Nth second pulse signal is received is determined by Doppler velocity measurement; the third speed and the third attitude information of the measured carrier are corrected by the speed of the GNSS antenna at the time when the Nth second pulse signal is received by using the extended Kalman filtering algorithm to obtain the fourth speed and the fourth attitude information of the measured carrier at the time when the Nth second pulse signal is received; the second speed and the second attitude information of the measured carrier at the sampling time are corrected according to the fourth speed and the fourth attitude information of the measured carrier at the time when the Nth second pulse signal is received; and the track geometric parameters are determined according to the vertical displacement data, the corrected second speed and the second attitude information of the measured carrier, compared with the prior art, the IMU data, the speed of the GNSS antenna and the vertical displacement data are synchronously collected. The inertial recursion value is corrected by using the speed of the GNSS antenna, the accuracy of the speed and the attitude information of the measured carrier is improved, and the accuracy of the track geometric parameter measurement under equal spatial interval sampling is improved. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort. In the drawings:

[0022] Figure 1 The system architecture schematic diagram of the track geometric parameter real-time measurement method provided by the present application;

[0023] Figure 2 The system hardware structure schematic diagram of the track geometric parameter real-time measurement method provided by the present application;

[0024] Figure 3 The flowchart of the track geometric parameter real-time measurement method provided by the present application;

[0025] Figure 4 The sensor synchronization timing diagram provided by this invention;

[0026] Figure 5 A schematic diagram of the spatial amplitude-frequency characteristics provided by the present invention;

[0027] Figure 6 A flowchart illustrating the real-time measurement method for track geometry parameters provided by this invention;

[0028] Figure 7 A flowchart illustrating the real-time measurement method for track geometry parameters provided by this invention;

[0029] Figure 8 A flowchart illustrating the real-time measurement method for track geometry parameters provided by this invention;

[0030] Figure 9 This is a schematic diagram of the structure of the real-time measurement device for track geometry parameters provided by the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0032] The real-time measurement method for track geometry parameters provided in this embodiment of the invention can be applied to, for example... Figure 1 The system architecture shown includes a data acquisition module 100 and a data processing module 200. The data acquisition module 100 includes an encoder 101, a visual ranging unit 102, an inertial measurement unit 103, and a GNSS unit 104.

[0033] The data acquisition module 100 counts the input pulses of the encoder 101. When the condition of equal spatial interval sampling is met, the visual ranging unit 102 is triggered to sample, and the data of the visual ranging unit 102, the inertial measurement unit 103 and the GNSS unit 104 are acquired in real time.

[0034] The data acquisition module 100 transmits the synchronized data to the data processing unit 200, which integrates the multi-source sensor data and outputs the measurement results of the track geometry parameters.

[0035] It should be noted that, Figure 1 This is merely an example of the system architecture of an embodiment of the present invention, and the present invention does not impose any specific limitations on it.

[0036] In a possible implementation, to improve the real-time performance of the interrupt response, the data acquisition unit adopts a QNX real-time operating system. The data acquisition module counts the input pulses of the encoder through a field programmable logic gate array (FPGA), and the external FPGA chip ensures the real-time performance of the pulse response at high speed and reduces the load of the interrupt interface of the data acquisition unit chip.

[0037] An inertial measurement unit (IMU) is composed of a three-axis gyroscope and a three-axis accelerometer, the three-axis gyroscope is used to measure the three-dimensional angular velocity of the detection beam, and the three-axis accelerometer is used to measure the three-dimensional acceleration of the detection beam. An odometer realized based on an encoder is installed on a wheel and used to measure the running speed of the train.

[0038] As shown in Figure 2 , the visual ranging unit and the inertial measurement unit are both installed on a bogie, and the odometer is installed on a wheel. To ensure signal strength and stability, a GNSS antenna is installed on the roof of the train.

[0039] In a possible implementation, the receiver of the GNSS unit measures the three-dimensional absolute position coordinates of the GNSS antenna under WGS84 and the speed under the north celestial east coordinate system based on the Doppler velocity measurement principle.

[0040] In a possible implementation, the visual ranging unit is coupled with a CCD camera and a laser, and is used to measure the lateral and vertical displacement of the detection system relative to the track plane.

[0041] Based on the system architecture shown above, Figure 3 a flowchart of a track geometric parameter real-time measurement method provided by an embodiment of the present application is shown in Figure 3 , and the method comprises the following steps.

[0042] In step 301, IMU data and vertical displacement data are collected at equal spatial intervals.

[0043] It should be noted that the vertical displacement data is the distance between the ranging sensor and the track top point.

[0044] In step 302, the first speed and the first attitude information of the measured carrier corresponding to each sampling time are determined according to the IMU data.

[0045] In step 303, the first speed and the first attitude information of the measured carrier corresponding to each sampling time are corrected by using an extended Kalman filtering algorithm to obtain the second speed and the second attitude information of the measured carrier at the sampling time.

[0046] In a possible implementation, a speed of the measured carrier at each sampling time is determined by using a speedometer, and the first speed and the first attitude information of the measured carrier are corrected according to the speed of the measured carrier at each sampling time collected by the speedometer.

[0047] In the embodiment of the application, the speed of the wheel set in the carrier coordinate system is calculated according to the cumulative mileage in the 2 sampling time measured by the speedometer, the first speed and the first attitude information of the measured carrier are corrected at each sampling time by using the non-integral constraint property, and the specific process is as follows:

[0048] The running speed is calculated according to the mileage in the 2 sampling time and the sampling time interval counted by the embedded board card.

[0049] The measurement error of the preset state quantity is determined by using the extended Kalman filtering algorithm according to the observation matrix.

[0050] The first speed and the first attitude information of the measured carrier are corrected according to the measurement error of the preset state quantity, and the second speed and the second attitude information of the measured carrier at the sampling time are obtained.

[0051] In step 304, when the Nth second pulse signal is received, the Nth time interval between the time when the Nth second pulse signal is received and the adjacent sampling time is determined.

[0052] In step 305, the third speed and the third attitude information of the measured carrier corresponding to the time when the Nth second pulse signal is received are determined by inertial recursion according to the Nth time interval and the IMU data of the adjacent sampling time.

[0053] In the embodiment of the application, before the inertial recursion according to the Nth time interval and the IMU data of the adjacent sampling time, the linear scaling factor is determined according to the time when the Nth second pulse signal is received and the time when the N-1th second pulse signal is received.

[0054] The Nth time interval is corrected according to the linear scaling factor.

[0055] In the embodiment of the application, the linear scaling compensation of the frequency error drift of the crystal oscillator is used to obtain the accurate local time without error accumulation. The linear scaling factor S c The scaling factor will compensate the frequency drift error of the crystal oscillator in the embedded board card in the next whole second, and the crystal oscillator clock deviation is corrected by using the PPS signal, so that the system time can be calibrated on the hardware level.

[0056] The linear scaling factor S cThe time between samples (TBS) can be corrected, and the specific calculation formula of the linear scale factor is as follows:

[0057]

[0058] Step 306, the velocity of the global navigation satellite system (GNSS) antenna corresponding to the time when the Nth second pulse signal is received is determined by Doppler velocity measurement.

[0059] Step 307, the third speed and the third attitude information of the measured carrier are corrected according to the velocity of the GNSS antenna corresponding to the time when the Nth second pulse signal is received by using the extended Kalman filtering algorithm, and the fourth speed and the fourth attitude information of the measured carrier corresponding to the time when the Nth second pulse signal is received are obtained.

[0060] Wherein, N is a positive integer greater than 1.

[0061] Step 308, the second speed and the second attitude information of the measured carrier at the sampling time are corrected according to the fourth speed and the fourth attitude information of the measured carrier corresponding to the time when the Nth second pulse signal is received.

[0062] Step 309, the track geometric parameters are determined according to the vertical displacement data, the corrected second speed and the second attitude information of the measured carrier.

[0063] The above scheme synchronously collects the IMU data, the velocity of the GNSS antenna and the vertical displacement data. The velocity of the GNSS antenna is used to correct the inertial recursive value, so as to improve the accuracy of the speed and attitude information of the measured carrier, and improve the accuracy of the track geometric parameter measurement under equal space interval sampling.

[0064] The inertial measurement unit (IMU) is the core sensor of the system for measuring track long wave irregularities. Since the track geometric parameters are output with distance as the horizontal coordinate, it is particularly important to convert the IMU data output at a fixed sampling rate to a space correlation sequence without distortion.

[0065] In the embodiment of the application, the IMU data is preprocessed by using cumulative filtering, and the time sequence of all IMUs in 2 sampling intervals is accumulated. Since the IMU sampling time and the system space sampling time usually do not coincide, it is necessary to linearly interpolate the angular velocity measurement value, and the sensor synchronization timing diagram is as follows: Figure 4 The angular increment Δθ k And the velocity increment Δv k Can be expressed as:

[0066]

[0067] Wherein, ω i is the gyro measurement value, and ai is the acceleration measurement value, δt k is the time interval between the time of the second pulse signal and the time of the adjacent sampling.

[0068] The above scheme converts the angular velocity and acceleration output in the time domain to the space domain, obtains the IMU space correlation sequence by using the cumulative filtering method, realizes the filtering method for converting the IMU data time sequence to the space sequence, and the space amplitude-frequency characteristic of the pre-processing method is represented as Figure 5 As shown in the figure, the system presents a low-pass filter characteristic, the high-frequency signals with a wavelength within 1 m are attenuated to different degrees, and the zero point is close to the sampling interval 0.25 m.

[0069] In step 303, the first speed and the first attitude information of the measured carrier corresponding to the time of the Nth second pulse signal are determined according to the IMU data of the Nth time interval and the time of the adjacent sampling, and the step flow is as shown in Figure 6 The specific process is as follows:

[0070] In step 601, the equivalent rotation vector algorithm is used to pre-process the IMU data of the adjacent sampling time, and the equivalent rotation vector is obtained.

[0071] In step 602, the third speed and the third attitude information of the measured carrier are obtained by inertial recursion according to the equivalent rotation vector and the Nth time interval.

[0072] In the embodiment of the application, when the angular velocity direction of the gyroscope carrier changes with time instead of doing a fixed-axis rotation, the differential equation describing the attitude motion is nonlinear, and directly using the sensor angular increment measurement value into the attitude and speed solving calculation will produce non-commutative error. Therefore, the equivalent rotation vector algorithm is used to pre-process the IMU data, the angular increment at the last sampling time is set as Δθ k-1 , and the angular increment measured at the current time is Δθ k . The equivalent rotation vector can be expressed as:

[0073]

[0074] The inertial recursion is performed according to the equivalent rotation vector and the time interval, and the specific calculation formula is as follows:

[0075]

[0076] wherein, is the rotation matrix at k time, and the current attitude information of the measured carrier can be solved; is the speed of the measured carrier in the navigation coordinate system at k time; b g is the zero offset of the gyroscope.

[0077] The third speed and the third attitude information of the measured carrier obtained by the above calculation are affected by sensor noise, which causes drift in the integration process and gradually accumulates errors. Therefore, the extended Kalman filtering algorithm is used to fuse the data of the inertial measurement unit, the odometer and the GNSS unit, and the third speed and the third attitude information of the measured carrier are corrected.

[0078] Firstly, the state quantity x of the system is determined as:

[0079]

[0080] In the embodiment of the application, the extended Kalman filtering algorithm aims to estimate the measurement error of the state quantity and feed back to the state quantity for correction.

[0081] Based on this, in step 305, the extended Kalman filtering algorithm is used to correct the third speed and the third attitude information of the measured carrier according to the speed of the GNSS antenna corresponding to the time when the Nth second pulse signal is received, and the fourth speed and the fourth attitude information of the measured carrier are obtained. The step flow is as shown in Figure 7 , and the specific steps are as follows:

[0082] In step 701, the observation matrix is determined according to the speed of the GNSS antenna corresponding to the time when the Nth second pulse signal is received.

[0083] In step 702, the preset measurement error of the state quantity is determined by using the extended Kalman filtering algorithm according to the observation matrix.

[0084] In step 703, the third speed and the third attitude information of the measured carrier are corrected according to the preset measurement error of the state quantity, and the fourth speed and the fourth attitude information of the measured carrier are obtained.

[0085] In the embodiment of the application, the speed of the GNSS antenna in the navigation coordinate system is measured to observe the state quantity.

[0086] Due to the large delay of the GNSS unit, in order to solve the waiting problem caused by the long time of GNSS information acquisition and measurement update calculation, the mechanical arrangement-prediction and combined update can be divided into two independent tasks with different priorities. If the delay of the GNSS is 100ms, the train speed is 360km / h, and the inertial calculation period is 2.5ms, although the delay has reached 40 inertial epochs, it does not affect the accuracy of the overall measurement estimation. When calculating and updating, the sequential filtering method or the time slicing filtering method can be used.

[0087] In the embodiment of the present application, after the velocity of the GNSS antenna corresponding to the time when the Nth second pulse signal is received is obtained, the measurement error of the preset state quantity is calculated and fed back to the state quantity at the current time, and the covariance matrix is transferred to the current time.

[0088] The measurement error is corrected to the state quantity to obtain more accurate attitude angle and velocity, and the specific calculation formula is as follows:

[0089]

[0090] The above scheme uses the extended Kalman filtering algorithm to correct the inertial recursive value, improves the accuracy of the velocity and attitude information of the measured carrier, and improves the accuracy of the orbital geometric parameter measurement under equal spatial interval sampling.

[0091] In step 306 of the embodiment of the present application, the orbital geometric parameter is determined according to the vertical displacement data, the second velocity and the second attitude information of the measured carrier, and the step flow is as shown in Figure 8 , and the specific process is as follows:

[0092] In step 801, the track irregularity data is determined according to the vertical displacement data and the corrected second velocity of the measured carrier.

[0093] In step 802, the inclination data of the track is determined according to the vertical displacement data and the corrected second attitude information of the measured carrier.

[0094] After the corrected second velocity and the second attitude information of the measured carrier are obtained in the embodiment of the present application, the orbital geometric parameter is solved. The rotation matrix is converted to Euler angle, wherein the roll angle is θ b . The specific calculation formula of the orbital geometric parameter is as follows:

[0095]

[0096] Wherein, pro is the high-low irregularity, d is the vertical displacement, G is the track gauge value, is the velocity in the z-axis direction of the navigation coordinate system, and c is the track superelevation value. The track horizontal, triangular pit and other geometric parameters are obtained according to c.

[0097] In a possible implementation, the real-time orbital geometric parameter measurement method provided by the embodiment of the present application is processed according to the following steps:

[0098] S1, when the system is powered on, the system is stationary for 5 minutes to perform initial alignment of the attitude of the measured carrier; the initial position of the measured carrier is determined according to the GNSS unit; and the coordinated universal time (UTC) time corresponding to the PPS pulse signal is determined.

[0099] S2, when the train starts, the FPGA chip outputs a high level to the ARM chip through the GPIO to trigger a system interrupt when the driving distance meets the sampling condition; the ARM outputs a rising edge signal to trigger the CCD camera, and receives IMU data and vision ranging unit data through the RS422 serial port; the time interval TBS of two sampling times is calculated.

[0100] S3, the data acquisition module packs and sends the IMU data, vertical displacement data and TBS to the data processing module; the data processing module first performs inertial recursion according to the IMU data, then observes the state quantity using the speed of the GNSS antenna, and calculates the measured carrier attitude information; then, the track geometric parameters are calculated according to the measured carrier attitude information and output.

[0101] S4, when the data acquisition module receives the second pulse signal of the GNSS unit through the GPIO, a QNX system interrupt is triggered, and the time interval between the received second pulse signal and the adjacent sampling time is calculated.

[0102] S5, at the next sampling time, the uncertainty covariance matrix, state quantity, time interval and UTC time of the IMU inertial recursion at the last sampling time are saved, and then step S3 is performed.

[0103] S6, when the data acquisition module receives a serial port interrupt, the observation information of the GNSS unit is analyzed.

[0104] S7, according to the UTC time, the uncertainty covariance matrix and state quantity in the state queue are found, the state quantity is corrected using the observation information of the GNSS unit, the measured carrier attitude is calculated, and then the track geometric parameters (high, low, track, super high, track spacing, etc.) are calculated and output. Repeat step S3.

[0105] In the embodiment of the application, a track geometric parameter real-time measurement device is also provided, as described in the following embodiment. As shown in the figure, Figure 9 The device comprises:

[0106] The data acquisition module 901 is used to acquire inertial measurement unit (IMU) data and vertical displacement data at equal spatial intervals; the vertical displacement data is the distance of the ranging sensor from the track top point;

[0107] The data processing module 902 is configured to: determine first speed and first attitude information of the measured carrier corresponding to each sampling time according to the IMU data; correct the first speed and the first attitude information of the measured carrier corresponding to each sampling time by using an extended Kalman filtering algorithm to obtain second speed and second attitude information of the measured carrier at the sampling time; determine an Nth time interval between a time when the Nth second pulse signal is received and an adjacent sampling time when the Nth second pulse signal is received; determine third speed and third attitude information of the measured carrier corresponding to the time when the Nth second pulse signal is received by performing inertial recursion according to the Nth time interval and the IMU data of the adjacent sampling time; determine the speed of the GNSS antenna corresponding to the time when the Nth second pulse signal is received by using Doppler velocity measurement; correct the third speed and the third attitude information of the measured carrier by using the extended Kalman filtering algorithm according to the speed of the GNSS antenna corresponding to the time when the Nth second pulse signal is received, to obtain fourth speed and fourth attitude information of the measured carrier corresponding to the Nth second pulse signal; wherein N is a positive integer greater than 1; correct the second speed and the second attitude information of the measured carrier at the sampling time according to the fourth speed and the fourth attitude information of the measured carrier corresponding to the Nth second pulse signal; and determine the track geometric parameters according to the vertical displacement data, the corrected second speed and the second attitude information of the measured carrier.

[0108] In the embodiment of the present application, the data processing module 902 is specifically configured to:

[0109] The IMU data of the adjacent sampling time is preprocessed by using an equivalent rotation vector algorithm to obtain an equivalent rotation vector.

[0110] The third speed and the third attitude information of the measured carrier are obtained by performing inertial recursion according to the equivalent rotation vector and the Nth time interval.

[0111] In the embodiment of the present application, the data processing module 902 is specifically configured to:

[0112] The observation matrix is determined according to the speed of the GNSS antenna corresponding to the time when the Nth second pulse signal is received.

[0113] The measurement error of the preset state quantity is determined by using the extended Kalman filtering algorithm according to the observation matrix.

[0114] The fourth speed and the fourth attitude information of the measured carrier are obtained by correcting the third speed and the third attitude information of the measured carrier according to the measurement error of the preset state quantity.

[0115] In the embodiment of the present application, the data processing module 902 is specifically configured to:

[0116] According to the vertical displacement data and the second speed of the measured carrier after correction, track irregularity data is determined.

[0117] According to the vertical displacement data and the second attitude information of the measured carrier after correction, inclination data of the track is determined.

[0118] In the embodiment of the present application, the data processing module 902 is further used for:

[0119] Before performing inertial recursion according to the IMU data of the Nth time interval and the adjacent sampling time, a linear proportion coefficient is determined according to the time when the Nth second pulse signal is received and the time when the N-1th second pulse signal is received;

[0120] The Nth time interval is corrected according to the linear proportion coefficient.

[0121] Since the principle of solving the problem of the device is similar to the real-time track geometric parameter measurement method, the implementation of the device can refer to the implementation of the real-time track geometric parameter measurement method, and the repeated parts will not be described again.

[0122] The embodiment of the present application also provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor realizes the above-mentioned real-time track geometric parameter measurement method when executing the computer program.

[0123] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the above-mentioned real-time track geometric parameter measurement method.

[0124] The embodiment of the present application also provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to realize the above-mentioned real-time track geometric parameter measurement method.

[0125] In the embodiment of the present application, the inertial measurement unit (IMU) data and the vertical displacement data are collected at equal spatial intervals; the vertical displacement data is the distance of the ranging sensor from the track top point; the first speed and the first attitude information of the measured carrier corresponding to each sampling time are determined according to the IMU data; the first speed and the first attitude information of the measured carrier corresponding to each sampling time are corrected to obtain the second speed and the second attitude information of the measured carrier at the sampling time; when the Nth second pulse signal is received, the Nth time interval between the time when the Nth second pulse signal is received and the adjacent sampling time is determined; the third speed and the third attitude information of the measured carrier corresponding to the time when the Nth second pulse signal is received are determined by inertial recursion according to the Nth time interval and the IMU data of the adjacent sampling time; the speed of the global navigation satellite system (GNSS) antenna corresponding to the time when the Nth second pulse signal is received is determined by Doppler velocity measurement; the third speed and the third attitude information of the measured carrier are corrected according to the speed of the GNSS antenna corresponding to the time when the Nth second pulse signal is received by using the extended Kalman filtering algorithm to obtain the fourth speed and the fourth attitude information of the measured carrier corresponding to the Nth second pulse signal; the second speed and the second attitude information of the measured carrier at the sampling time are corrected according to the fourth speed and the fourth attitude information of the measured carrier corresponding to the Nth second pulse signal; and the track geometric parameters are determined according to the vertical displacement data, the corrected second speed and the second attitude information of the measured carrier, compared with the prior art, the IMU data, the speed of the GNSS antenna and the vertical displacement data are synchronously collected; the inertial recursion value is corrected by using the speed of the GNSS antenna, the accuracy of the speed and the attitude information of the measured carrier is improved, and the accuracy of the track geometric parameter measurement under equal spatial interval sampling is improved.

[0126] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can adopt a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0127] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0128] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0129] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0130] The above-described specific embodiments are merely intended to further describe the purpose, technical solutions and beneficial effects of the present application, and it should be understood that the above-described specific embodiments are merely specific embodiments of the present application and are not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for real-time measurement of track geometry parameters, characterized in that, The method comprises the following steps: Collecting inertial measurement unit (IMU) data and vertical displacement data at equal spatial intervals; the vertical displacement data is the distance of a ranging sensor from the track top point; Determining the first speed and first attitude information of the measured carrier corresponding to each sampling time according to the IMU data; Correcting the first speed and first attitude information of the measured carrier corresponding to each sampling time by using an extended Kalman filtering algorithm to obtain the second speed and second attitude information of the measured carrier at the sampling time; When the Nth second pulse signal is received, determining the Nth time interval between the time when the Nth second pulse signal is received and the adjacent sampling time; Determining the third speed and third attitude information of the measured carrier corresponding to the time when the Nth second pulse signal is received by inertial recursion according to the Nth time interval and the IMU data of the adjacent sampling time; Determining the speed of a global navigation satellite system (GNSS) antenna corresponding to the time when the Nth second pulse signal is received by using Doppler velocity measurement; Correcting the third speed and third attitude information of the measured carrier by using an extended Kalman filtering algorithm according to the speed of the GNSS antenna corresponding to the time when the Nth second pulse signal is received to obtain the fourth speed and fourth attitude information of the measured carrier corresponding to the Nth second pulse signal; wherein N is a positive integer greater than 1; the second speed and second attitude information of the measured carrier at the sampling time are corrected according to the fourth speed and fourth attitude information of the measured carrier corresponding to the Nth second pulse signal; Determining the track geometric parameters according to the vertical displacement data, the corrected second speed and second attitude information of the measured carrier; Correcting the first speed and first attitude information of the measured carrier corresponding to each sampling time by using an extended Kalman filtering algorithm to obtain the second speed and second attitude information of the measured carrier at the sampling time, comprising: Calculating the driving speed according to the mileage and sampling time interval within 2 sampling times counted by the embedded board card; Determining the measurement error of the preset state quantity by using an extended Kalman filtering algorithm according to an observation matrix; Correcting the first speed and first attitude information of the measured carrier according to the measurement error of the preset state quantity to obtain the second speed and second attitude information of the measured carrier at the sampling time.

2. The method of claim 1, wherein the track geometry parameter is measured in real time. Determining the third speed and third attitude information of the measured carrier corresponding to the time when the Nth second pulse signal is received by inertial recursion according to the Nth time interval and the IMU data of the adjacent sampling time, comprising: Preprocessing the IMU data of the adjacent sampling time by using an equivalent rotation vector algorithm to obtain an equivalent rotation vector; Performing inertial recursion according to the equivalent rotation vector and the Nth time interval to obtain the third speed and third attitude information of the measured carrier.

3. The method of claim 1, wherein the track geometry parameter is measured in real time. Correcting the third speed and third attitude information of the measured carrier by using an extended Kalman filtering algorithm according to the speed of the GNSS antenna corresponding to the time when the Nth second pulse signal is received to obtain the fourth speed and fourth attitude information of the measured carrier, comprising: Determining an observation matrix according to the speed of the GNSS antenna corresponding to the time when the Nth second pulse signal is received; Determining the measurement error of the preset state quantity by using an extended Kalman filtering algorithm according to the observation matrix; The third speed and the third attitude information of the measured carrier are corrected according to the measurement error of the preset state quantity, and fourth speed and fourth attitude information of the measured carrier are obtained.

4. The method of claim 1, wherein the track geometry parameter is measured in real time. The track geometric parameters are determined according to the vertical displacement data, the corrected second speed and the second attitude information of the measured carrier, including: The track irregularity data are determined according to the vertical displacement data and the corrected second speed of the measured carrier; The inclination data of the track are determined according to the vertical displacement data and the corrected second attitude information of the measured carrier.

5. The method of claim 1, wherein the track geometry parameter is measured in real time. Before the inertial recursion is performed according to the IMU data of the Nth time interval and the adjacent sampling time, further comprising: The linear proportion coefficient is determined according to the time when the Nth second pulse signal is received and the time when the N-1th second pulse signal is received; The Nth time interval is corrected according to the linear proportion coefficient.

6. A device for real-time measurement of track geometry parameters, characterized in that Comprising: The data acquisition module is used for collecting inertial measurement unit (IMU) data and vertical displacement data at equal spatial intervals; the vertical displacement data is the distance from the track top point to the track top point of the distance sensor; The data processing module is used for determining the first speed and the first attitude information of the measured carrier corresponding to each sampling time according to the IMU data; the first speed and the first attitude information of the measured carrier corresponding to each sampling time are corrected by using an extended Kalman filtering algorithm, to obtain the second speed and the second attitude information of the measured carrier at the sampling time; when the Nth second pulse signal is received, the Nth time interval between the time when the Nth second pulse signal is received and the adjacent sampling time is determined; the third speed and the third attitude information of the measured carrier corresponding to the time when the Nth second pulse signal is received are determined by performing inertial recursion according to the Nth time interval and the IMU data of the adjacent sampling time; the speed of the global navigation satellite system (GNSS) antenna corresponding to the time when the Nth second pulse signal is received is determined by using Doppler velocity measurement; the third speed and the third attitude information of the measured carrier are corrected according to the speed of the GNSS antenna corresponding to the time when the Nth second pulse signal is received by using an extended Kalman filtering algorithm, to obtain the fourth speed and the fourth attitude information of the measured carrier corresponding to the time when the Nth second pulse signal is received; wherein N is a positive integer greater than 1; the second speed and the second attitude information of the measured carrier at the sampling time are corrected according to the fourth speed and the fourth attitude information of the measured carrier corresponding to the time when the Nth second pulse signal is received; the track geometric parameters are determined according to the vertical displacement data, the corrected second speed and the second attitude information of the measured carrier; The data processing module is specifically used for: The driving speed is calculated according to the mileage and the sampling time interval within the 2 sampling times counted by the embedded board; The measurement error of the preset state quantity is determined according to the observation matrix by using an extended Kalman filtering algorithm; The first speed and the first attitude information of the measured carrier are corrected according to the measurement error of the preset state quantity, to obtain the second speed and the second attitude information of the measured carrier at the sampling time.

7. The track geometry real-time measuring device according to claim 6, c h a r a c t e r i z e d b y that The data processing module is specifically used for: The IMU data of the adjacent sampling time are preprocessed by using an equivalent rotation vector algorithm, to obtain an equivalent rotation vector; According to the equivalent rotation vector and inertial recursion in the Nth time interval, third speed and third attitude information of the measured carrier are obtained.

8. The track geometry real-time measuring device according to claim 6, wherein The data processing module is specifically configured to: determine an observation matrix according to the speed of the GNSS antenna corresponding to the moment when the Nth second pulse signal is received; determine the measurement error of the preset state quantity according to the observation matrix by using an extended Kalman filtering algorithm; correct the third speed and the third attitude information of the measured carrier according to the measurement error of the preset state quantity, and obtain fourth speed and fourth attitude information of the measured carrier.

9. The track geometry real-time measuring device according to claim 6, wherein The data processing module is specifically configured to: determine track irregularity data according to the vertical displacement data and the corrected second speed of the measured carrier; determine the inclination data of the track according to the vertical displacement data and the corrected second attitude information of the measured carrier.

10. The track geometry real-time measuring device according to claim 6, wherein The data processing module is further configured to: determine a linear proportionality coefficient according to the moment when the Nth second pulse signal is received and the moment when the N-1th second pulse signal is received before performing inertial recursion according to the IMU data of the Nth time interval and the adjacent sampling moment; correct the Nth time interval according to the linear proportionality coefficient.

11. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the method of any one of claims 1 to 5 when executing the computer program.

12. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method of any one of claims 1 to 5.

13. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program is executed by the processor to implement the method of any one of claims 1 to 5.

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